from keras.preprocessing.image import load_img, img_to_array
target_image_path = ''
style_reference_image_path = ''
width,height = load_img(target_image_path).size
img_height = 400
img_width = int(width*img_height/height)

import numpy as np
from keras.applications import vgg19


def prepocess_image(image_path):
    img = load_img(image_path, target_size=(img_height, img_width))
    img = img_to_array(img)
    img = np.expand_dims(img, axis=0)
    img = vgg19.preprocess_input(img)
    return img


def deprocess_image(x):
    x[:, :, 0] += 103.939
    x[:, :, 1] += 116.779
    x[:, :, 2] += 123.68
    x = x[:, :, ::-1]
    x = np.clip(x, 0, 255).astype('uint8')
    return x


from keras import backend as K
target_image = K.constant(prepocess_image(target_image_path))
style_reference_image = K.constant(prepocess_image(style_reference_image_path))
combination_image = K.placeholder((1, img_height, img_width, 3))
input_tensor = K.concatenate([target_image, style_reference_image, combination_image], axis=0)
model = vgg19.VGG19(input_tensor=input_tensor,weights='imagenet', include_top=False)
print('Model loaded')
def content_loss(base,combination):
    return K.sum(K.square(combination-base))

def gram_matrix(x):
    features = K.batch_flatten(K.permute_dimensions(x, (2, 0, 1)))
    gram = K.dot(features, K.transpose(features))
    return gram

def style_loss(style, combination):
    S = gram_matrix(style)
    C = gram_matrix(combination)
    channels = 3
    size = img_width * img_height
    return K.sum(K.square(S-C)/4.*(channels**2)*(size**2))

def total_variation_loss(x):
    a = K.square(
        x[:, :img_height-1, :img_width-1, :] -
        x[:, 1:, :img_width -1, :]
    )
    b = K.square(
        x[:, :img_height - 1, :img_width - 1, :] -
        x[:, :img_height - 1, 1:, :]
    )
    return K.sum(K.pow(a+b), 1.25)

output_dict = dict([(layer.name, layer.output) for layer in model.layers])
content_layer = 'block5_conv2'
style_layers = ['block1_conv1',
                'block2_conv1',
                'block3_conv1',
                'block4_conv1',
                'block5_conv1']
total_variation_weight = 1e-4
style_weight = 1.
content_weight = 0.025

loss = K.variable(0.)
layer_features = output_dict[content_layer]
target_image_features = layer_features[0.:,:,:]
combination_features = layer_features[2.:,:,:]
loss += content_weight*content_loss(target_image_features,combination_features)

for layer_name in style_layers:
    layer_features = output_dict[layer_name]
    style_reference_features = layer_features[1, :,:,:]
    combination_features = layer_features[2, :,:,:]
    sl = style_loss(style_reference_features,combination_features)
    loss += ((style_weight/style_layers)) * sl

loss += total_variation_weight*total_variation_loss(combination_image)

grads = K.gradients(loss, combination_image)[0]
fetch_loss_and_grads = K.function([combination_image], [loss, grads])

class Evaluator(object):
    def __init__(self):
        self.loss_value = None
        self.grads_values = None

    def loss(self,x):
        assert self.loss_value is None
        x = x.reshape((1, img_height, img_width, 3))
        outs = fetch_loss_and_grads([x])
        loss_value = outs[0]
        grad_values = outs[1].flatten().astype('float64')
        self.loss_value = loss_value
        self.grads_values = grad_values
        return self.loss_value
    def grads(self,x):
        assert self.loss_value is not None
        grad_values = np.copy(self.grads_values)
        self.loss_value = None
        self.grads_values = None
        return grad_values
evaluator = Evaluator()

from scipy.optimize import fmin_l_bfgs_b
import scipy.misc
import time
result_prefix = 'myresult'
iterations = 20
x = prepocess_image(target_image_path)
x = x.flatten()
for i in range(iterations):
    print('Start of iteration',i)
    start_time = time.time()
    x, min_val, info = fmin_l_bfgs_b(evaluator.loss,x,fprime=evaluator.grads,maxfun=20)
    print('Current loss value:', min_val)
    img = x.copy().reshape((img_height, img_width, 3))
    img = deprocess_image(img)
    fname = result_prefix + '_at_iteration_d%.png'%i
    scipy.misc.imsve




